Strata-NeRF : Neural Radiance Fields for Stratified Scenes

Ankit Dhiman, R Srinath, Harsh Rangwani, Rishubh Parihar, Lokesh R Boregowda, Srinath Sridhar, R Venkatesh Babu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 17603-17614

Neural Radiance Fields (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settings concentrate on 3D modelling a single object or a single level of a scene. However, in the real world, a person captures a structure at multiple levels, resulting in layered capture. For example, tourists usually capture a monument's exterior structure before capturing the inner structure. Modelling such scenes in 3D with seamless switching between levels can drastically improve the Virtual Reality (VR) experience. However, most of the existing techniques struggle in modelling such scenes. Hence, we propose Strata-NeRF, a single radiance field that can implicitly learn the 3D representation of outer, inner, and subsequent levels. Strata-NeRF achieves this by conditioning the NeRFs on Vector Quantized (VQ) latents which allows sudden changes in scene structure with changes in levels due to their discrete nature. We first investigate the proposed approach's effectiveness by modelling a novel multilayered synthetic dataset comprising diverse scenes and then further validate its generalization on the real-world RealEstate dataset. We find that Strata-NeRF effectively models the scene structure, minimizes artefacts and synthesizes high-fidelity views compared to existing state-of-the-art approaches in the literature.